The storing and manipulation of large amounts of data is becoming ever more important. It can be important for data to be consistent within a particular data storage system, or between different data storage systems. As data is manipulated, there is the chance to produce data, or modify source data, that contains inconsistencies. In some cases, data inconsistencies can refer to differences between what should be the same or a related data item stored in two or more locations. In other cases, data inconsistencies can refer to data that does not adhere to one or more rules relating to the data, such as formatting of the data or relationships with other data items.
Data inconsistencies can be difficult to detect, and can have negative impacts on users and companies. Typically, data inconsistencies are not discovered until a user reports a problem or an error. In some cases, the provider of a database system or other data management program, or in house support personnel, are contacted to determine the source of the problem and to determine the severity of the issue because it may not be known how many users or data items may be subject to the problem. To try and isolate the source of the problem, a software developer or other individual with appropriate technical expertise can write a report, such as a program or script, to apply check methods to determine whether data is consistent, and the source of any inconsistency. However, these reports are typically created for very specific circumstances, are very labor intensive, and are often not used after initial investigation, and potentially solution, of the problem. Thus, there remains room for improvement in the analysis of data, including analysis for consistency.